class_docstr: There are only a couple of modifications needed to
  use huggingface:
  - we add a bunch of attributes corresponding to the batch items,
      redefining mb_x and mb_y too
  - _unpack_minibatch sends the dictionary values to the GPU device
  - forward and criterion are adapted for machine translation tasks.
docstr: Current mini-batch target.
docstr_tok: ["Current","mini-batch","target","."]
code: def mb_y(self):
      """Current mini-batch target."""
      return self.mbatch["labels"]
  
imports: from avalanche.benchmarks.utils import DataAttribute, ConstantSequence
  from avalanche.training.plugins import ReplayPlugin
  from dataclasses import dataclass
  from transformers import PreTrainedTokenizerBase
  from typing import Optional, Union, Any
  from transformers.utils import PaddingStrategy
  import torch
  import avalanche
  import torch.nn
  from avalanche.benchmarks import CLScenario, CLStream, CLExperience
  from avalanche.evaluation.metrics import accuracy_metrics
  import avalanche.training.templates.base
  from avalanche.benchmarks.utils import AvalancheDataset
  from transformers import AutoTokenizer
  from transformers import T5ForConditionalGeneration
  from datasets import load_dataset
  import numpy
label_desc: Use image node_id 3 for calling the HGNaive obj's underlying member method code with example usage: obj.mb_y() and returns: self